Technical Field
[0001] The present invention relates to a destination-prediction apparatus, a navigation
apparatus such as a car navigation system or a mobile phone to which the destination-prediction
apparatus is applied, and a destination-prediction method. The destination-prediction
apparatus predicts a route that a user will take or a destination such as a goal on
the basis of trip history data in a mobile terminal installed in a vehicle or carried
by the user.
Background Art
[0002] Conventionally, there is a disclosed example of a technique applied to a navigation
apparatus to be installed in a vehicle. The navigation apparatus predicts, on the
basis of a past trip history of the vehicle, a goal for which the vehicle is heading.
The trip history includes start and end points of trips and days and times of the
trips (See Patent Reference 1, for example). According to Patent Reference 1, the
navigation apparatus searches a past trip history for a record of moving at "20:30,
Thursday" when, for example, a driver is traveling in a vehicle at 20:30 on a Thursday.
In the case where a search result shows that the vehicle moved to a landmark A three
times and a landmark B once at corresponding times and on corresponding days in the
past, the landmark A is predicted to be a goal with a high probability of 75%. In
the case where there is no record of moving at "20:30, Thursday" in the past, the
condition for searching for a history record is eased to, for example, "20:30, weekday"
to extract an appropriate record, so that a landmark to be a goal is predicted similarly
on the basis of trip frequency. In this example, a "day" of Thursday, is generalized
to a larger set (hereinafter referred to as class) of "weekdays". Similarly, the "time"
of 20:30 may be categorized into a defined class such as "evening (18:00 to 24:00)"
for the purpose of calculating a prediction probability on the basis of frequency
using more history records. Using such classes for prediction is effective not only
for the case where there is no record to satisfy a condition but also from viewpoint
of increasing reliability of a prediction probability, because referring to more data
items in a calculation of probability generally increases reliability.
Patent Reference 1: Japanese Unexamined Patent Application Publication No. 2005-156350
Disclosure of Invention
Problems that Invention is to Solve
[0003] There is a problem, however, with a method of the conventional technique of predicting
a destination using only frequencies of trips that belong to the same class as a trip
for which a destination is to be predicted. For example, when a trip history of "Monday
morning" has 20 records of a trip to the landmark A and 10 records of a trip to the
landmark B, the landmark A is predicted to be a goal with the highest probability
of 67%. On the other hand, in the case where a user has a characteristic of making
a trip to the landmark A only once a day, a navigation apparatus predicts that the
user will make a trip to the landmark A with the highest probability at 8:00 on a
Monday when the user starts an engine. However, even after the user has moved to the
landmark A and starts the engine again at 11:00 in this case, the navigation apparatus
will predict the landmark A again regardless of the user's characteristic of making
a trip to the landmark A only once a day, because the user moves to the landmark A
most frequently in view of a class of "Monday morning". The problem is that highly
accurate prediction cannot be achieved using a method as described above where prediction
is based on only high and low frequencies found in a searched history that has a similar
trip condition to a trip for which a destination is predicted.
[0004] The present invention, conceived to address the problem with the conventional technique
described above, has an object of providing a destination-prediction apparatus, a
navigation apparatus, and the like that predict a destination with higher accuracy
than the conventional technique.
Means to Solve the Problems
[0005] In order to develop a technique to predict a destination (a goal or an intersection
to be moved to) using a trip history, the inventors conducted an experiment on approximately
30 subjects in order to collect their trip histories by vehicles for three months
for the purpose of verification of prediction accuracy. Hereinafter, the experiment
is described with reference to FIGS. 1 to 6.
[0006] In the experiment, frequencies of trips to goals to which one of the subjects, Subject
I made trips from "Home" during a period classed as "12:00 to 18:00, days-off (Saturday,
Sunday, etc.)" has been accumulated for three months. FIG. 1 lists some of the goals
in standings in descending order of frequency. As shown in FIG. 1, a trip to the "Learning
center A" occurred most frequently, six times. This is followed by a round trip from
"Home" to "Home" without cutting an engine and a trip from "Home" to the "Parking
A", both of which occurred at the same frequency of four times. Shown at the bottom
of the table is a trip, which occurred twice, to the "Supermarket A".
[0007] FIG. 2 shows trip frequencies per unit of "a day" with respect to Subject I again.
These trip frequencies are sorted by landmarks to be goals and by months. Specifically,
in the first month, Subject I made a trip to the "Learning center A" once in "a day"
on eight days, but not on a single day twice or three times or more in "a day". Similarly,
in the second month, Subject I made the trip there once in a day on some days, but
not twice or more on any days. This shows that Subject I made the trip to "Learning
center A" once in "a day" at the maximum. In contrast, Subject I made a trip to the
"Hospital A" once in "a day" on one day, and twice on another day. This shows that
there is no fixed frequency of the trip to the "Hospital A" in "a day".
[0008] FIG. 3 lists actual trips of the Subject I again in the order of occurrence. These
trips occurred between 12:00 and 18:00 on Saturday, November 5, 2005. A trip with
an ID 1 indicates that Subject I left home at 12:23 for the learning center A. By
using the prediction method according to Patent Reference 1 for predicting a goal
at the departure from home, the goal is predicted to be the learning center A with
the highest probability or the home or the parking A with the second highest probability
on the basis of the frequency information in FIG. 1. An application that uses such
a prediction result and displays names of goals (with the three highest probabilities)
along with estimated times of arrivals at the goals on a car navigation apparatus
will present a screen shown in FIG. 4 to Subject I.FIG. 4 shows a screen to be displayed
to the Subject I on a display of a car navigation by an application that uses such
a prediction result and lists names of goals (with the three highest probabilities)
along with estimated times of arrivals at the goals.
Subject I actually had a trip of the ID 1 to a goal of the learning center A. This
prediction thus proves to be successful.
[0009] Then, the Subject I had a trip of an ID 2 departing the learning center A at 15:03
back for home, and had another trip of an ID 3 departing home at 17:08. By using the
conventional prediction method, a goal of this trip is predicted to be the learning
center A with the highest probability again as in the case with the trip of ID 1.
In this case, the Subject I has a screen as shown in FIG. 5.
[0010] However, this prediction proves to be unsuccessful because the Subject I actually
went to the supermarket A in the trip. One of the reasons for this is that such prediction
is not based on consideration of a characteristic of the Subject I regarding the trip
frequency that can be found for "a day" with respect to each of the landmarks. To
put it another way, considering the characteristic shown in FIG. 2, the prediction
would prove to be successful by excluding the learning center A from prediction for
the trip of ID 3 and promoting the supermarket A, the fourth most frequent goal in
FIG. 1, instead so that a screen in FIG. 6 could be presented to the Subject I.
[0011] The present invention increases prediction accuracy by utilizing, as rules, characteristics
of frequencies of trips to landmarks, intersections, or streets that are goals to
which a user has moved during a predetermined period.
[0012] In order to address the problem with the conventional technique, the destination-prediction
apparatus according to the present invention predicts a destination of a moving object,
and includes: an obtaining unit configured to obtain trip history data indicating
a trip history of the moving object; a position detecting unit configured to detect
a present position of the moving object; a candidate-destination selecting unit configured
to search the trip history data for trip history records containing the present position
of the moving object and select a plurality of candidate destinations and set an order
thereof on a basis of frequencies of the trip history records retrieved in the search,
the trip history data being obtained by the obtaining unit, and the present position
being detected by the position detecting unit; a display unit configured to display,
according to the order, the plurality of candidate destinations selected by the candidate-destination
selecting unit; a rule setting unit configured to judge, with regard to at least each
of the plurality of candidate destinations, whether or not there is a rule that the
moving object moves to the candidate destination at a constant trip frequency during
a certain unit period, and set a rule that includes the unit period and the trip frequency
when there is the rule; a unit-period frequency accumulating unit configured to specify,
with reference to the trip history data, an actual frequency with regard to one of
the plurality of the candidate destinations with regard to which the rule setting
unit judges that there is the rule, the actual frequency being a frequency at which
the moving object has moved to the candidate destination during the unit period; and
an actual-frequency judging unit configured to make a judgment, with regard to each
of the plurality of candidate destinations, on whether or not the actual frequency
specified by the unit-period frequency accumulating unit satisfies the rule by judging
whether or not the actual frequency has reached the trip frequency included in the
rule, the display unit being configured to display the plurality of candidate destinations
so that a result of the judgment is reflected thereon when the actual-frequency judging
unit judges that the actual frequency satisfies the rule. The display unit is a functional
device that is a combination of the drawing processing unit 117 and the display unit
106 in the embodiments described below.
[0013] This configuration will achieve prediction of a destination with a high accuracy
using a user's regularity of frequency of trips to destinations such as a landmark,
an intersection, and a road in a predetermined unit period.
[0014] Here, the destination-prediction apparatus preferably further includes a display
unit configured to display the plurality of candidate destinations according to the
order changed by the actual-frequency judging unit
[0015] This configuration will allow a user to make use of a finite display area efficiently
and prevent the user from browsing or listening to unnecessary information by avoiding
presenting such unnecessary information to the user visually or audibly.
[0016] The present invention may be implemented not only as the destination-prediction apparatus,
but also as a navigation apparatus including the destination-prediction apparatus,
a destination-prediction method, a computer-executable program, or a computer-readable
storage medium, such as a CD-ROM, that stores such a program.
Effects of the Invention
[0017] In order to achieve high-accuracy prediction, the destination-prediction apparatus
according to the present invention predicts a destination on the basis of trip history
data, taking two trip probabilities into consideration. One trip probability is based
on past trip frequency under a condition similar to that of movement for which a destination
is being predicted, and the other is based on a rule about frequency of a trip to
a destination (such as a landmark, an intersection, or a road) in a predetermined
unit period.
[0018] The use will be thus shown only destinations with high probability, instead having
information unnecessary to the user. The present invention has a great deal of practical
value for securing usability for users and safety for driving, especially when applied
as a car navigation apparatus.
Brief Description of Drawings
[0019]
[FIG. 1] FIG. 1 shows a goal to be a destination in a class and actual data regarding
frequency to there.
[FIG. 2] FIG. 2 shows actual data regarding characteristics of frequencies of trips
to each landmark in a predetermined period.
[FIG. 3] FIG. 3 shows actual data that indicates trips on a day chronologically.
[FIG. 4] FIG. 4 shows an example of a screen in an application when using a prediction
method according to a conventional technique.
[FIG. 5] FIG. 5 shows an example of a screen in an application that fails in prediction
when using a prediction method according to a conventional technique.
[FIG. 6] FIG. 6 shows an example of a screen in an application that succeeds in prediction
when using a prediction method that is based on a rule about trip frequency in a predetermined
period.
[FIG. 7] FIG. 7 shows an external view of a navigation apparatus according to the
first embodiment of the present invention.
[FIG. 8] FIG. 8 shows a configuration of a navigation apparatus according to the first
embodiment of the present invention.
[FIG. 9] FIG. 9 shows map data stored in a storage unit. FIG. 9 (A) shows node data.
FIG. 9 (B) shows supplemental node data. FIG. 9 (C) shows link data.
[FIG. 10] FIG. 10 shows landmark data stored in the storage unit. FIG. 10 (A) shows
facility data. FIG. 10 (B) shows user-configured data.
[FIG. 11] FIG. 11 shows trip history data stored in the storage unit.
[FIG. 12] FIG. 12 shows period rule data stored in the storage unit.
[FIG. 13] FIG. 13 is a flowchart that shows flow of process by a period setting unit
and a rule extracting unit according to the first embodiment of the present invention.
[FIG. 14] FIGS. 14 (A) and (B) show examples of actual frequencies of trips to destinations
in a predetermined period.
[FIG. 15] FIG. 15 is a flowchart that shows flow of process in which a candidate-destination
selecting unit selects destinations.
[FIG. 16] FIG. 16 shows day classes and time classes to be used in the process of
selecting a candidate destination.
[FIG. 17] FIG. 17 shows an example of relevant information displayed on the display
unit on the basis of a result of destination prediction according to the present invention.
[FIG. 18] FIG. 18 shows an example of relevant information displayed on the display
unit on the basis of a result of destination prediction according to the present invention.
[FIG. 19] FIG. 19 shows an example of relevant information displayed on the display
unit on the basis of a result of destination prediction according to the conventional
technique.
[FIG. 20] FIG. 20 shows an example of relevant information displayed on the display
unit on the basis of a result of destination prediction according to the present invention.
[FIG. 21] FIG. 21 is a flowchart that shows flow of process from a start of a trip
to its end by a navigation apparatus according to the first embodiment of the present
invention.
[FIG. 22] FIG. 22 shows an example of relevant information displayed on the display
unit on the basis of a result of destination prediction.
[FIG. 23] FIG. 23 is a configuration of a navigation apparatus according to the second
embodiment of the present invention.
[FIG. 24] FIG. 24 shows an example of actual frequency of a trip to a destination
in a predetermined period.
[FIG. 25] FIG. 25 is a flowchart that shows flow of process in which a rule is set
according to the second embodiment of the present invention.
[FIG. 26] FIG. 26 is a configuration of a navigation apparatus according to the third
embodiment of the present invention.
[FIG. 27] FIG. 27 shows general-frequency-rule data stored in the storage unit.
[FIG. 28] FIG. 28 is a flowchart that shows flow of process in which the general-frequency-rule
data is statistically calculated according to the third embodiment of the present
invention.
[FIG. 29] FIG. 29 shows examples of actual histories of trips for each user and each
destination in a predetermined period.
[FIG. 30] FIG. 30 shows an example of numbers of people who have actually made a trip
to each destination in a predetermined period by frequency.
[FIG. 31] FIG. 31 is a flowchart that shows flow of process from a start of a trip
to its end by a navigation apparatus according to the third embodiment of the present
invention.
[FIG. 32] FIG. 32 illustrates a different destination-prediction method.
[FIG. 33] FIG. 33 shows an example of relevant information displayed on the display
unit on the basis of a result of destination prediction according to the conventional
technique or the first embodiment of the present invention.
[FIG. 34] FIG. 34 shows an example of relevant information displayed on the display
unit on the basis of a result of destination prediction according to the different
destination-predicting method.
[FIG. 35] FIG. 35 shows another example of relevant information displayed on the display
unit.
[FIG. 36] FIG. 36 shows another example of relevant information displayed on the display
unit.
[FIG. 37] FIG. 37 shows an example of a definition of a unit period.
[FIG. 38] FIGS. 38 (a) and (b) show processes that depend on unit periods of different
definitions.
Numerical References
[0020]
- 101, 101a, 101b
- Navigation apparatus
- 102
- Day-and-time detecting unit
- 103
- Position detecting unit
- 104
- Storage unit
- 105
- Control unit
- 106
- Display unit
- 107
- Landmark data
- 108
- Map data
- 109
- Trip history data
- 110
- Data handling unit
- 111
- Matching unit
- 112
- Candidate-destination selecting unit
- 113, 113a
- Period setting unit
- 114
- Rule extracting unit
- 115
- Unit-period frequency accumulating unit
- 116
- Actual-frequency judging unit
- 117
- Drawing processing unit
- 120
- Period rule data
- 130, 130a, 130b
- Rule setting unit
- 131
- Maximum-frequency determining unit
- 1801
- General-frequency-rule data
Best Mode for Carrying Out the Invention
[0021] Hereinafter, embodiments of the present invention is described with reference to
figures.
(First Embodiment)
[0022] First described is the first embodiment of the present invention.
[0023] FIG. 7 is an outline view of a navigation apparatus 101 that is an embodiment of
a destination-prediction apparatus according to the present invention. The navigation
apparatus 101, which is installed in a car, is a navigation apparatus functions as
a destination-prediction apparatus according to the present invention and displays
a predicted destination.
[0024] FIG. 8 shows a configuration of the navigation apparatus 101 according to the first
embodiment of the present invention. The navigation apparatus 101 is a car navigation
apparatus capable of predicting a destination with a high accuracy using regularity
of a user (or a moving object) that moves to a destination at a constant trip frequency
during a certain unit period. This navigation apparatus includes a day-and-time detecting
unit 102, a position detecting unit 103, a storage unit 104, a control unit 105, and
a display unit 106.
[0025] The day-and-time detecting unit 102, which may be a calendar-equipped clock, detects
a present day and time.
[0026] The position detecting unit 103 is installed in a vehicle provided with the navigation
apparatus 101 and detects a present position, a speed, orientation, or a present time.
The position detecting unit 103 is, for example, a global navigation satellite system
(GNSS) receiver, a vehicle speed sensor, a gyroscope (or an angular velocity sensor),
or an acceleration sensor. The GNSS receiver measures and determines an absolute position
of the receiver by demodulating radio waves received from a plurality of satellites.
The GNSS receiver is, for example, a GPS receiver. The GNSS receiver and these sensors
are used alone or in combination for determining the present position, the speed,
and the orientation.
[0027] The display unit 106 displays an image according to display image data generated
by the control unit 105. The display unit 106 is, for example, a liquid-crystal display
or an organic EL display.
[0028] The storage unit 104 stores map information such as data of roads, intersections,
and landmarks, and a trip history of a vehicle that is provided with the navigation
apparatus 101. The storage unit 104 is, for example, a hard disk drive (HDD), an optical
storage medium such as digital versatile disk (DVD), or another storage medium such
as a flash memory. These storage media may be configured to be detachable. The storage
unit 104 may also be of a configuration where information to be stored is downloaded
as necessary from a server system (not shown) through a communication unit not shown
here (a mobile phone, a PHS, for example).
[0029] Data to be stored in the storage unit 104 includes map data 108, landmark data 107,
trip history data 109, and period rule data 120.
[0030] FIG. 9 shows excerpt data of information related to the first embodiment from map
information stored in the map data 108. The map data 108 includes (A) node data, (B)
supplemental node data, and (C) link data. The (A) node data indicates a point such
as an intersection or a confluence where roads branch off in some directions. Each
node data item is composed pf position information such as a latitude and a longitude,
number of links linked to the node, and an ID of the link. The links are described
later. The (B) supplemental node data indicates a bending point on a link, which is
described later, when the link is not straight. Each supplemental node data item is
composed of position information such as a latitude and a longitude and an ID of a
link on which the supplemental node data is. The (C) link data indicates a road that
connects one node to another. Each link data item is composed of a start point node
and an end point node which are ends of a link, length of the link (in meters, kilometers,
or the like), width of the link (in meters or the like), a road type such as a general
road or an expressway, the number of the aforementioned supplemental nodes and IDs
thereof.
[0031] FIG. 10 shows information stored in the landmark data 107. The (A) facility data
contains landmarks registered by a manufacturer of the navigation apparatus 101, an
information service provider, or like that. Each Landmark item is composed of a landmark
name, category of the landmark, a uniquely assigned ID, and position information such
as a latitude and a longitude. On the other hand, each landmark item registered in
(B) user-configured data by a user is composed of a landmark name, a uniquely assigned
ID, and position information such as a latitude and a longitude. Unlike the landmark
name in the (A) facility data, the user may register names meaningful to the user
as the landmark names in the (B) user-configured data, such as "Home" and "Office".
[0032] FIG. 11 shows an example of data stored in the trip history data 109. The trip history
data 109 stores a history of present positions that are detected by the position detecting
unit and associated with present days and times detected by the day-and-time detecting
unit 102. While the user is making a trip in the vehicle, the day-and-time detecting
unit 102 detects days and times, and the position detecting unit 103 detects positions
of the vehicle. The trip history data 109 stores information about the days and times
of the trip and the positions of the vehicle for a segment from start of the trip
to end of it, that is, a start point, a goal, and a route, chronologically. The position
information detected by the position detecting unit 103 is translated to an ID such
as of a landmark or a link where the vehicle is situated by an after-mentioned matching
unit 111 of the control unit 105. The trip history data 109 stores the ID information.
For example, the trip history at the top of FIG. 11 indicates that the vehicle departed
a landmark of an ID LM201 at 8:43 on January 19, 2007, moved along a road of a link
ID L3 at 8:45, a road of a link ID 9 at 8:50, a road of a link ID 12 at 8:56, and
links not shown, to reach a goal, a landmark of a landmark ID 202 at 9:25.
[0033] The start of the trip of the vehicle can be detected with a start of an engine, and
the end of the trip with a stop of the engine. Although the link IDs of the roads
along which the vehicle moved are stored as information for the route in this example,
node IDs that indicate intersections by which the vehicle passes by may be stored
instead.
[0034] FIG. 12 shows an example of data stored in the period rule data 120. The period rule
data 120 stores rules about periods. The rules are referred to by the rule setting
unit 130 when it extracts a rule about frequency of a trip to each of the landmarks
as described later. As shown in FIG. 12, priority has been established for application
of the rules. The highest priority is given to attempting to extract a rule of trip
frequency for a period of "a day". In the case where no rule for the period of "a
day" is extracted as a result of following a process by the rule setting unit 130
described later, another attempt will be made to extract a rule of trip frequency
for a period with the second highest priority, "weekdays" or "days-off". Similarly,
there are period rules that are subsequently applied according to the priority. The
highest priority for the rules in the period rule data 120 is given to the rule for
the shortest period, "a day", and lower application priorities to the rules for the
longer period such as "a week". A reason for this will be given later in detail.
[0035] The control unit 105 is implemented with a CPU or an MPU which controls entire operation
of the navigation apparatus 101, and a read only memory (ROM), a random access memory
(RAM) or the like. From a viewpoint of functions, the control unit 105 includes a
data handling unit 110, a matching unit 111, a candidate-destination selecting unit
112, a rule setting unit 130, a unit-period frequency accumulating unit 115, an actual-frequency
judging unit 116, and a drawing processing unit 117. Hereinafter, each of these components
is described in detail.
[0036] The data handling unit 110 functions as a processing unit that handles data transfer
between the other components of the control unit 105 and the storage unit 104, such
as accumulation of trip history data in the storage unit 104 or read and acquisition
of data 107 to 120 from the storage unit 104. Processes by the data handling unit
110 includes transferring data from the landmark data 107 or the map data 108 to the
matching unit 111 when the matching unit 111 executes map-matching process, accumulating
IDs of map-matched position information with day-and-time information detected by
the day-and-time detecting unit 102 in the trip history data 109, searching the trip
history data 109 or the period rule data 120 when processes necessary for destination
prediction is executed, and transferring map data from the storage unit 104 to the
drawing processing unit 117 which displays the map data on the display unit 106. These
processes will be described later in detail.
[0037] The matching unit 111, referring to the landmark data 107 or the map data 108 obtained
through the data handling unit 110, executes map-matching process to translate the
position information detected by the position detecting unit 103 to IDs. The map-matching
process is a function already in practical use for a conventional navigation apparatus
and, accordingly, will not be described in the present description in detail.
[0038] The candidate-destination selecting unit 112 operates in conjunction with the matching
unit 111 in order to search the trip history data 109 stored in the storage unit 104
via the data handling unit 110 for a trip history record that contains the present
position of the moving object detected by the position detecting unit 103. The candidate-destination
selecting unit 112 then selects a plurality of candidate destinations and set an order
of the plurality of candidate destinations on the basis of frequency of the retrieved
trip history record. Specifically, in order to select the candidates, the candidate-destination
selecting unit 112 searches the trip history data 109 for destinations where the user
moved to on the same day and time and at the same position in the past, referring
to day-and-time information detected by the day-and-time detecting unit and position
information map-matched by the matching unit 111.
[0039] The rule deciding unit 130 is a processing unit that has a period setting unit 113
and a rule extracting unit 114 and judges, with respect to at least each of the plurality
of the candidate destinations selected by the candidate-destination selecting unit
112, whether a rule that the moving object moves to the candidate destination at a
constant trip frequency in a certain unit period is recognized. The rule deciding
unit 130 then sets a rule using the unit period and the trip frequency when the rule
is recognized.
[0040] The period setting unit 113 is a processing unit that sets a unit period for specifying
a rule. The period setting unit 113, referring to the period rules stored in the period
rule data 120, sets a period as a unit for a rule when a rule about trip frequency
for a destination such as a landmark, a road, or an intersection is extracted from
the trip history data 109. The unit may be, for example, "a day" for "Move to the
landmark A once in a day" or "a week" for "Move along Route 1 four times in a week".
[0041] The rule extracting unit 114 is a processing unit that sets (or extracts) a specified
frequency as a rule, in other words, makes a rule for the unit period set by the period
setting unit 113 using the trip frequency for each destination. The rule extracting
unit 114 specifies, by analyzing the trip history 109, the frequency of trip of the
moving object to each of the plurality of the candidate destinations selected by the
candidate-destination selecting unit 112 during the unit period set by the period
setting unit 113.
[0042] Flow of process by the period setting unit 113 and the rule extracting unit 114 is
described in detail with reference to a flowchart shown in FIG. 13. First, the period
setting unit 113 refers to the trip history data 109 and extract one of the IDs of
the destinations (S101). Although goals or routes in the trip history data 109 are
counted as the destinations, here described is a case where the IDs of the goals are
extracted. When the extracted destination ID is "Shop A", the period setting unit
113 then generates a table by calculating daily frequency of a trip of the user to
the "Shop A" from a trip history data of a predetermined range (four weeks, for example)
of the trip history data 109 (S102). FIG. 14 (A) shows an example of the generated
table. As shown in FIG. 14 (A), the table stores trip frequencies for each of 28 days,
or four weeks from Monday to Sunday, trip frequencies for each of the weeks in view
of a class of weekdays from Monday to Friday, trip frequencies for each of the weeks
in view of a class of days-off of Saturday and Sunday, and trip frequencies for each
of the weeks from Monday to Sunday, that is, sums of trip frequencies for weekdays
and days-off.
[0043] Subsequently, the period setting unit 113 refers to the table to judges whether or
not trip frequency for the unit of "a day" that has the highest priority stored in
the period rule data 120 is constant (S103). FIG. 14 (A) shows that the user moves
to the "Shop A" once on some days but not on other days. This proves that the trip
frequency is not constant. When judging that the trip frequency in not constant (No
in S103), the period setting unit 113 then judges whether or not trip frequency for
the unit of "weekdays" or "days-off" that has the second highest priority stored in
the period rule data 120 is constant (S104). For "days-off", the user makes a trip
to there once in some weeks but not in other weeks, without a constant frequency.
For "weekdays", however, the user makes a trip to there four times in all the weeks
with a constant frequency. When judging that the trip frequency is constant (Yes in
S104), the period setting unit 113 sets "weekdays" as a period to be used for extracting
a rule for frequency of the trip to the "Shop A" (S107). Following the setting of
the period by the period setting unit 113, the rule extracting unit 114 extracts a
rule of "four times for weekdays" for the frequency of the trip to the "Shop A" (S108).
[0044] The period setting unit 113 subsequently judges whether or not the there is still
a destination for which a period has not been set in the trip history data 109 (S109).
When there is (Yes in S109), the period setting unit 113 selects a new destination
(S101). When the newly selected destination is, for example, "Rest A", the period
setting unit 113 generates a frequency table for the "Rest A" (S102). FIG. 14 (B)
shows a frequency table for the "Rest A".
[0045] The period setting unit 113 newly judges, with respect to the new destination, whether
or not trip frequency for the unit of "a day" that has the highest priority stored
in the period rule data 120 is constant (S103). FIG. 14 (B) shows that the user moves
to the "Rest A" once on some days but not on other days. This proves that the trip
frequency is not constant. When judging that that the trip frequency in not constant
(No in S103), the period setting unit 113 judges whether or not trip frequency for
the unit of "weekdays" or "days-off" that has the second highest priority stored in
the period rule data 120 is constant (S104). The frequency is not constant either
for "weekdays" or "days-off". When judging the trip frequency is not constant (No
in S104), the period setting unit 113 then judges whether or not trip frequency for
the unit of "a week" that has the third highest priority stored in the period rule
data 120 is constant. For the unit of "a week", the user moves to the "Rest A" twice
every week. This proves that the frequency is constant. When judging that the trip
frequency is constant (Yes in S104), the period setting unit 113 sets "a week" as
a period to be used for extracting a rule for the frequency of the trip to the "Rest
A" (S107). Following the setting of the period by the period setting unit 113, the
rule extracting unit 111 extracts a rule of "twice in a week" for the frequency of
the trip to the "Rest A" (S108).
[0046] The period setting unit 113 subsequently judges whether or not there is still a destination
for which a period has not been set in the trip history data 109 (S109). Such a process
is executed again when there is (Yes in S109). Otherwise (No in S109), the process
is ended.
[0047] In the case where there is no constant trip frequency for "a day", "weekday or days-off",
or "a week" (No in S105), the rule extracting unit 114 judges that there is no rule
about frequency for the destination, and the period setting unit 113 judges whether
or not there is still a destination for which such a judgment has not been made yet
(S109).
[0048] This is how the period setting unit 113 refers to the trip history data 109 stored
in the storage unit 104 via the data handling unit 110 and thereby specifies a period
for which there is constant frequency of a trip of the moving object to a candidate
destination and sets the specified period as a unit period.
[0049] Although trip history data of four weeks is used as a reference by the period setting
unit 113 for setting a unit period such as "a day", "weekdays or days-off", or "a
week" in the first embodiment, a history of any days can be used as a reference history.
Further, a period other than the above, such as "ten days", "a month", "three months",
"a half year", or " a year", may be considered to be a period about which the rules
stored in the period rule data 120 are. It is noted that a reference history of a
period longer than a month is necessary for extracting a rule for a unit period of
"a month" or longer.
[0050] In addition, although the first embodiment describes an example where a trip frequency
rule is extracted for a specific landmark such as the "Shop A" or the "Rest A", a
rule can be also extracted not for such a specific facility but for a category. Categories
such as "restaurants" or "supermarkets" to which facilities belong can be specified
by referring to the landmark data 107 in FIG. 10. Trip frequency for each category
can be thus calculated by aggregating frequencies of trips to respective facilities.
This enables extracting a rule such as "once for days-off" for the category of "restaurants".
[0051] Such extracting of a rule for a category is effective especially in the following
case. Considering a trip for shopping by a housewife, for example, the housewife may
usually have a plurality of shops to go for shopping, such as "Shop A", "Shop B",
and "Shop C". On a basis of a period of a week, frequency of trips to these shops
may fluctuate as follows: twice for the "Shop A", twice for the "Shop B", and once
for the "Shop C" in a week; once for the "Shop A", twice for the "Shop B", and twice
for the "Shop C" in another week; and once for the "Shop A", once for the "Shop B",
and three times for the "Shop C" in another week. In this case, a rule of trip frequency
may not be always extracted for a specific landmark. However, a rule of trip frequency
of five times in a week can be extracted not for any of the landmarks but for the
category of "supermarkets" to which these landmarks belong. Such process can be executed
in order to judge whether or not a trip rule can be extracted from a viewpoint of
a category to which a specific landmark belongs in the case where no trip rule for
the landmark can be extracted.
[0052] Although the example describes that the period setting unit 113 judges the trip frequency
for weekdays to be constant when, for example, four weekly frequencies in a history
of four weeks are identical, definition of "constant" may be expanded as follows.
For example, when referring to a history of ten weeks shows that a trip to a destination
was made four times for a span of weekdays in eight weeks, three times in one week,
and five times in one week, this is judged to be "not constant" according to the original
definition. With a predetermined threshold (80%, for example), the frequency may be
judged to be "constant" when weeks more than the threshold have identical frequencies.
In this example, such trip was made four times in eight weeks out of ten, that is,
more than the threshold of 80%; therefore, the frequency is judged to be constant,
and a rule of "four times for weekdays" may be extracted for the destination. A rule
can be extracted thereby when such a tendency (regularity) is recognizable even when
not all trip frequencies are identical.
[0053] In the case where a rule of "five times for weekdays" is extracted while the user
moves to there once on every day from Monday to Friday on a basis of a unit of "a
day", the period "weekdays"" and "a day" can be used in combination to set a rule
such as "once a day for weekdays". Using such a combined rule will enable configuring
a rule about trip frequency for increased accuracy.
[0054] The unit-period frequency accumulating unit 115 is a processing unit that specifies
an actual frequency at which the moving object moved to the candidate destination
during a unit period with reference to the trip history data 109 with regard to at
least a candidate destination for which the rule setting unit 130 judges that there
is a rule. The trip history data 109 is stored in the storage unit 104 and referred
to via the data handling unit 110. The candidate destination is among a plurality
of candidate destinations selected by the candidate destination selecting unit 112.
Specifically, the unit-period frequency accumulating unit 115 accumulates a number
of times that the moving object moved to the candidate destination during the unit
period set by the period setting unit 113. For example, when the period setting unit
113 sets a unit period of "a week" which is assumed to start on Monday with regard
to the "Rest A", the unit-period frequency accumulating unit 115 increments the frequency
at every detection of moving to the "Rest A" from Monday. The accumulated frequency
is reset to zero at the end of the unit period, that is, at the end of Sunday. The
frequency is incremented from next Monday again at every detection of such moving.
[0055] FIG. 15 is a flowchart that shows process of the candidate-destination selecting
unit 112 in detail. The number of candidates in the flowchart is assumed to be five.
[0056] First, the candidate-destination unit 112 refers to the trip history data 109 stored
in the storage unit 104 via the data handling unit 110 in order to search for records
corresponding to the present trip in terms of the position when the engine is started
or the position information of the vehicle has become mismatched from the output from
the matching unit 111 (for example, when the vehicle has moved from a link of an ID
5 to a link of an ID 3) (S201). The candidate-destination unit 112 then searches the
history for records that has the same position information of the vehicle as the present
position information in the case where the engine is started, and for records that
share both a start point and a route up to the present position of the vehicle with
the present trip in the case where the position information has become mismatched.
[0057] When the number of the corresponding trip records is not more than a predetermined
value (for example, ten) (No in S202), the candidate-destination selecting unit 112
refers to the entire trip history data 109 and selects destinations with the five
highest trip frequencies among all the records there as candidate destinations (S203),
and then ends the process.
[0058] On the other hand, when the number of records of the corresponding trip is not less
than the predetermined value (Yes in S202), the candidate-destination selecting unit
112 further searches the records retrieved in the step S201 for corresponding records
in terms of both a day class and a time class (S204). The day class and the time class
are described below with reference to FIG. 16. Such classes are used to grasp a given
case with a wider concept. There are day classes such as "weekdays" class and a "days-off"
class. The "weekdays" class contains instances of Monday, Tuesday, Wednesday, Thursday,
and Friday. The "days-off" class contains instances of Saturday and Sunday. There
are four time classes of a "late night" class, a "morning" class, an "afternoon" class,
and an "evening" class. The "late night" class is set to range from 0:00 to 6:00,
the "morning" class from 6:00 to 12:00, the "afternoon" class from 12:00 to 18:00,
and the "evening" from 18:00 to 24:00. Given a case of "11:58 on Monday", a corresponding
day class is the "weekdays" class, and a corresponding time class is the "morning"
class. Given a case of "12:30 on Monday", a corresponding day class is the "weekdays"
class, and a corresponding time class is the "afternoon" class. The candidate-destination
selecting unit 112 refers to class definitions as shown in FIG. 16 so as to search
for a record that is of corresponding day and time classes.
[0059] When history records not less than a predetermined value are retrieved in the step
S204 (Yes in S205), the candidate-destination selecting unit 112 selects, as candidate
destinations, destinations with the five highest trip frequencies among the records
corresponding to the present trip in terms of the day class and the time class as
well as the position, (S206), and then ends the process.
[0060] On the other hand, when the number of records is found not more than the predetermined
value (No in S205), the candidate-destination selecting unit 112 searches the records
retrieved in the step S201 and corresponding to the present trip in terms of the position
for corresponding records in terms of either the day class or the time class (S207).
When the number of corresponding records in terms of either of these classes are not
less than a predetermined value (Yes in S208), the candidate-destination selecting
unit 112 selects, as candidate destinations, destinations with the five highest trip
frequencies among the records of one class for which more records have been found
than the other class (S209), and then ends the process. When the number of records
are found not more than a predetermined value for either class (No in S208), the candidate-destination
selecting unit 112 selects, as candidate destinations, destinations of the five highest
trip frequencies among the records retrieved in the step S201 and corresponding to
the present trip in terms of the position (S210), and then ends the process.
[0061] The actual-frequency judging unit 116 judges whether or not the actual frequency
satisfies the rule by judging whether or not the actual frequency specified by the
unit-period frequency accumulating unit 115 has reached the frequency included in
the rules with regard to each of the candidate destinations selected by the candidate-destination
selecting unit 112. For this judgment, the actual-frequency judging unit 116 uses
information regarding candidate destinations from the candidate-destination selecting
unit 112, the rules for the frequency of a trip to each of the destination during
the unit periods extracted by the rule extracting unit 114, and the actual frequency
of the trip to each of the destinations during the unit periods counted by the unit-period
frequency accumulating unit 115. Specifically, any of the following method is used
for predicting a destination: In one method, among a plurality of candidate destinations,
a prediction probability that the moving object moves to one candidate destination
for which the rule is satisfied is reduced below a prediction probability of another
candidate destination for which a rule is not satisfied. An order of the candidate
destinations selected by the candidate-destination selecting unit 112 is thereby changed
and the candidate destinations in the changed order are predicted to be a plurality
of destinations. In another method, the candidate-destination selecting unit 112 changes
an order of a plurality of candidate destinations by placing a lower priority on a
candidate destination for which a rule is satisfied than when the rule is not satisfied
for the candidate destination, or a higher priority on a candidate destination for
which a rule is not satisfied than when the rule is satisfied for the candidate destination.
In the other method, a candidate destination for which a rule is satisfied is excluded
from a plurality of candidate destinations selected by the candidate-destination selecting
unit 112 for the purpose of changing an order. Any of these plural methods may be
implemented in a navigation apparatus alone or in a manner that allows the user to
select one.
[0062] The drawing processing unit 117 executes process to draw on the display unit 106
information regarding a destination predicted by the actual-frequency judging unit
116 (in other words, given a final order) along with the map data 108. Specifically,
the drawing processing unit 117 displays candidate destinations selected by the candidate-destination
selecting unit 112 on the display unit 106 usually according to an ordering set by
the candidate-destination selecting unit 112. But when the actual-frequency judging
unit 116 has changed the order of the candidate destinations, the drawing processing
unit 117 controls display on the display unit 106 in order to present the plurality
of candidate destinations in the new ordering.
[0063] An operation example of the actual-frequency judging unit 116 is described with reference
to what is on the display unit 106 shown on FIGS. 17 and 18. The following conditions
are assumed here. Present time is 9:20 on Thursday. The five most probable candidate
destinations have been selected by the candidate-destination selecting unit 112: "School
D", 20 times; "Shop A", 15 times; "Park B", 8 times, "Hospital F", 4 times; and "Shop
B", twice. The rule extracting unit 114 has extracted a rule "four times for weekdays".
The unit-period frequency accumulating unit 115 has accumulated frequency of the trip
to the "Shop A" to "three times" for the week. The rule extracting unit 114 has extracted
no rule for candidate destinations other than the "Shop A". The display unit 106 displays
the three most probable candidate destinations with relevant information.
[0064] Under these conditions, the actual-frequency judging unit 116 first sets the five
candidate destinations selected by the candidate-destination selecting unit 112 as
candidates to display in a descending order of the frequencies. The actual-frequency
judging unit 116 then checks for a rule extracted by the rule extracting unit 114
with regard to each of the candidate destinations. In this case, there is a rule "four
times for weekdays" for the "Shop A". Finally, the actual-frequency judging unit 116
refers to the unit-period frequency accumulating unit 115 to find a frequency of "three
times" for the trip to the "Shop A". This proves that the rule extracted by the rule
extracting unit has not been satisfied yet with regard to any of the five destinations
selected as candidate destinations. Accordingly, the actual-frequency judging unit
116 predicts as destinations (finally ordered destinations) the three most probable
destinations among the ones selected by the candidate-destination selecting unit 112,
the "School D", the "Shop A", and the "Park B".
[0065] The drawing processing unit 117 displays these destinations with relevant information
on the display unit 106. FIG. 17 shows a display example. In the example shown in
FIG. 17, the relevant information is displayed under names of estimated arrival time
and notice. The trip history data 109 stores a route taken in the past to each destination.
Calculating of the estimated arrival time and selecting of the notice are based on
the length of the route or an average vehicle speed (for example, 80 km/h for expressways
and otherwise 30 km/h) for the case where it is assumed that the user takes the route,
as well as on traffic information or commercial information obtained through a VICS
receiving unit or an information receiving unit from a network, which are not shown
in FIG. 8, in the navigation apparatus.
[0066] Here are additional conditions that the user has moved to the "Shop A" in this trip,
and that the user has started the vehicle at 9:10 on the following day, Friday. In
this case, the five most probable candidate destinations selected by the candidate-destination
selecting unit 112 are assumed as follows: "School D", 20 times; "Shop A", 16 times
including one added trip; "Park B", 8 times; "Hospital F", 4 times; and "Shop B",
twice. The rule "four times for weekdays" extracted by the rule extracting unit 114
still holds true for the "Shop A", while the unit-period frequency accumulating unit
115 updates the frequency for the "Shop A" to four times. The rule extracting unit
114 has extracted no rule for candidate destinations other than the "Shop A".
[0067] Under these conditions, the actual-frequency judging unit 116 first sets the five
candidate destinations selected by the candidate-destination selecting unit 112 as
candidates to display in a descending order of the frequencies. The actual-frequency
judging unit 116 then checks for a rule extracted by the rule extracting unit 114
with regard to each of the candidate destinations. In this case, there is a rule "four
times for weekdays" for the "Shop A". Finally, the actual-frequency judging unit 116
refers to the unit-period frequency accumulating unit 115 to find a frequency of "four
times" for the trip to the "Shop A". This proves that the rule for the trip frequency
has already been satisfied with regard to the "Shop A" among the five destinations
selected as candidate destinations. Accordingly, the actual-frequency judging unit
116 predicts as destinations the three most probable destinations among the ones selected
by the candidate-destination selecting unit 112, the "School D", the "Park B", and
the "Hospital F", excluding the "Shop A". The drawing processing unit 117 displays
these destinations with relevant information on the display unit 106.
[0068] FIG. 18 shows a display example for this case. For comparison, FIG. 19 shows a result
of prediction which is based on only the trip frequency as in the case of the conventional
technique. As shown in FIG. 19, information relevant to the "Shop A", which has low
probability to be a goal on weekdays in the week, is displayed because a rule about
frequency of trips to the destinations during the unit period are not taken into consideration.
On the other hand, unlike FIG. 19, destinations and relevant information in FIG. 18,
a display example according to the present invention, does not include information
relevant to the "Shop A". This is because the rule about the frequency of the trips
to the destinations during the unit period, that is, the rule that the user moves
to the "Shop A" no more than "four times for weekdays" is taken into consideration
despite that destinations selected as candidate destinations according to the trip
frequency are the same.
[0069] The actual-frequency judging unit 116 thereby excludes a candidate destination that
satisfies a rule from a plurality of the candidate destinations in order to change
an order a plurality of candidate destinations selected by the candidate-destination
selecting unit 112. The drawing processing unit 117 thus displays the plurality of
candidate destinations remaining after the exclusion of a candidate destination by
the actual-frequency judging unit 116.
[0070] The destination that satisfies the rule about the trip frequency may not be excluded
from the display as in the first embodiment, but moved down in the order to be displayed.
For example, as the rule has been satisfied with regard to the "Shop A" that is predicted
to be the second most probable candidate in the first embodiment, the "Shop A" may
be moved down to the third of the three candidates to be displayed on the display
unit 106, with the "School D" at the top of the display, followed by the "Park B"
and the "Shop A" at the bottom. To put it another way, the actual-frequency judging
unit 116 thus change the order of the plurality of candidate destinations selected
by the candidate-destination selecting unit 112 so that the candidate destination
for which the rule has been satisfied will be displayed lower than the candidate for
which the rule has not been satisfied yet.
[0071] FIG. 20 is a display example for this case. The candidate destination remains displayed
even with decreased priority in FIG. 20 because the trip frequency for the destination
is high enough under the conditions. This increases usability even in the case where
tendency of frequency of a trip to a destination has changed but not been extracted
as a rule yet.
[0072] Display priority may be also increased for a destination when a trip frequency rule
for the destination has not been satisfied yet but another destination for which a
trip frequency rule has been satisfied is displayed higher than the destination. This
emphasizes to the user that there is still a destination for which the rule has not
been satisfied yet.
[0073] The following is a description of a process flow by the navigation apparatus 101
from a start of a trip to its end with reference to a flowchart in FIG. 21. The navigation
apparatus 101 is configured as described above.
[0074] The matching unit 111 executes map-matching process when the position detecting unit
103 detects position information (S301). In the case where the engine in the vehicle
is detected turned on or position of the vehicle changed (into a link with a different
ID, for example) (Yes in S302), the candidate-destination selecting unit 112 refers
to the day-and-time information detected by the day-and-time detecting unit 102, a
present position (or a trip route up to the present position) map-matched by the matching
unit 111, and the trip history data 109 in order to select candidate destinations
for the current trip (S303). When candidate destinations are selected, the actual-frequency
judging unit 116 refers to the rule extracting unit 114 for a rule for frequency of
trips to destinations in a group of the candidate destinations (S304). Meanwhile,
the actual-frequency judging unit 116 refers to the unit-period frequency accumulating
unit 115 for frequency at which the trip to the candidate destinations has been made
during unit periods (S305).
[0075] The actual-frequency judging unit 116 predicts a destination on the basis of outputs
from the candidate-destination selecting unit 112, the rule extracting unit 114, and
the unit-period frequency accumulating unit 115. The drawing processing unit 117,
following the prediction, executes drawing process for the display unit 106 (S307).
It is then judged whether or not the engine is turned off. In the case where the engine
is still running (No in S308), the process is repeated from the step S301. In the
case where the engine is turned off (Yes in S308), the data handling unit 110 accumulates
the trip in the trip history data 109. When the destination is a place stored in the
unit-period frequency unit 115, the data handling unit 110 increments the value by
one (S310), and the period setting unit 113 and the rule extracting unit 114 executes
process for updating the rule (S311).
[0076] The process of steps S309 to S311 can be executed when the navigation apparatus is
provided with electricity after the engine is stopped. This process, however, may
be executed in the next trip after the engine is started again if the navigation apparatus
is provided with electricity or, even when provided with electricity, not in operation.
[0077] In the first embodiment, the period setting unit 113 judges whether or not there
is a constant trip frequency for "a day", "weekdays or days-off", and "a week" in
the period rule data 120 that is used for setting unit periods. The judging process
is executed advancing from "a day", the shortest period, then "weekdays or days-off",
to "a week" the longest, because of the following advantage: First, the trip frequency
rules for these unit periods are not exclusive mutually. In other words, both of the
rules "once a day" and "seven times in a week" may be satisfied for a landmark A.
For "a week" from Monday to Sunday, the user is predicted, on the basis of the rule
of "once a day", to make a trip to the landmark A with a high probability in a trip
made on a Monday evening when the user has not been to the landmark A. Providing information
relevant to the landmark A will be convenient to the user. Such information will be
deduced and provided for the user on a daily basis, on Tuesday, Wednesday, and days
to come as well. On the other hand, under the rule of "seven times in a week", the
user who starts a trip on a Friday evening after four trips to the landmark A in the
week will be provided with no useful information because of the probability that the
user will make three remaining trips to there in the weekend. This is the reason giving
priority to extracting a rule for a unit period as short as possible is more beneficial.
[0078] The method for selecting a candidate destination by the candidate-destination selecting
unit 112 is not limited to the method described in the first embodiment. The method
may be the predicting method described in Patent Reference 1 or the method such as
disclosed in Japanese Patent No.
3722229 (Patent Reference 2) as a method for prediction with a good accuracy.
[0079] Although predicted destinations described in the first embodiment are landmarks which
will be goals of the trips by the users, destinations are not limited to such landmarks.
The destinations may be links (in other words, segments of roads) or nodes (branching
points such as intersections). In the case where information of cities, towns, or
villages in which the landmarks, links, or nodes are located is available as reference,
destinations to be predicted may include names, such as "Osaka City" or "in or near
Kyoto city".
[0080] When a frequency calculated by the unit-period frequency accumulating unit 115 satisfies
a frequency rule, which is extracted by the rule extracting unit 114, for a candidate
destination selected by the candidate-destination selecting unit, the actual-frequency
judging unit 116 may instruct the drawing processing unit 117 to display a message
to inform accordingly. For example, when a rule for the "Shop A" is "four times in
a week" and the five most probable candidates selected by the candidate-destination
selecting unit 112 includes the "Shop A", the actual-frequency judging unit 116 may
exclude the "Shop A" from the candidate destinations and the drawing processing unit
117 may output a message shown in FIG. 22 to the display unit 106. The user will be
thus reminded that the rule has been already satisfied and avoid a useless trip even
when the user is making a trip while being ignorant of it.
(Second Embodiment)
[0081] The second embodiment of the present invention is described below.
[0082] The first embodiment shows an example where the rule extracting unit 114 extracts
a rule of frequency using a unit period which is set by the period setting unit 113
and for which frequency of a trip to a destination is constant. The second embodiment
describes another method in which the rule extracting unit 114 extracts a rule.
[0083] FIG. 23 shows a configuration of a navigation apparatus 101a according to the second
embodiment. As in the one in the first embodiment, the navigation apparatus 101a is
a car navigation apparatus capable of predicting a destination with a high accuracy
using regularity of a user (or a moving object) that moves to a destination at a constant
trip frequency in a certain unit period. This navigation apparatus includes a day-and-time
detecting unit 102, a position detecting unit 103, a storage unit 104, a control unit
105, and a display unit 106.
[0084] Also the second embodiment describes an example of a destination-prediction apparatus
according to the present invention where the destination-prediction apparatus is applied
to the navigation apparatus 101a. Components that function similarly in the navigation
apparatus 101 in the first embodiment are numbered the same and given no description
in detail.
[0085] In the second embodiment, the rule setting unit 130 (the period setting unit 113
and the rule extracting unit 114) of the navigation apparatus 101 according to the
first embodiment is substituted by a rule setting unit 130a (a period setting unit
113a, a maximum-frequency determining unit 131, and a rule extracting unit 114) as
shown in FIG. 23.
[0086] The period setting unit 113a is a processing unit that sets a unit period to specify
a rule. In the second embodiment, the unit period is predetermined. The period setting
unit 113 extracts a period for which frequency of a trip to each destination is constant
in the first embodiment, whereas the period setting unit 113a predetermines units
to be used. There are four predetermined periods assumed in this case: "a day", "weekdays",
"days-off", and "a week". Specifically, the period setting unit 113a according to
the second embodiment constantly sets the four periods of "a day", "weekdays", "days-off",
and "a week" instead of searching for a unit period for which trip frequency is constant
by analyzing the trip history data 109.
[0087] The maximum-frequency determining unit 131 is a processing unit that analyzes the
trip history data 109 in order to determine a maximum frequency at which a moving
object moves to a candidate destination during the unit periods set by the period
setting unit 113a. The maximum-frequency determining unit 131 according to the second
embodiment refers to trip history data about each destination stored in the trip history
data 109 in order to calculate the maximum frequency of a trip to be used for rules
to be extracted by the rule extracting unit 114.
[0088] Two examples of calculation by the maximum-frequency determining unit 131 are described
below with regard to frequency of trip to the "Shop A" shown in FIG. 24.
(i) Calculating the maximum frequency during a period
[0089] The maximum-frequency determining unit 131 calculates a frequency largest in the
trip history data 109 that contains frequencies at which a moving object moves to
a candidate destination during a unit period set by the period setting unit 113a.
The maximum-frequency determining unit 131 then determines the calculated largest
frequency as a maximum. Specifically, the maximum-frequency determining unit 131 refers
to information on trip frequency shown in FIG. 24 and extracts the largest frequency
of a trip to a destination during a predetermined period (a unit period set by the
period setting unit 113a). For a period of "a day", three patterns of frequencies
of zero, one, and two can be found. The largest among them is two; thus, a value of
two is determined as the largest frequency for a period of "a day". Similarly, a value
of five is determined for a period of "weekdays", a value of one for "days-off", and
a value of five for a period of "a week".
(ii) Calculating the most frequent frequency during a period
[0090] The maximum-frequency determining unit 131 specifies numbers of occurrences of frequencies
in the trip history data 109 that contains the frequencies at which a moving object
moves to a candidate destination during a unit period set by the period setting unit
113a. The maximum-frequency determining unit 131 then determines a frequency having
the largest number of occurrences as the maximum. Specifically, the maximum-frequency
determining unit 131 refers to information on trip frequency shown in FIG. 24 and
extracts the most frequent frequency of a trip to a destination during a predetermined
period (a unit period set by the period setting unit 113a). For a period of "a day",
the trip was not made on 12 days, but once on 14 days and twice on two days; thus,
a value of one is determined as the most frequent frequency for a period of "a day".
Similarly, a value of four is determined for a period of "weekdays". For a period
of "days-off", the trip was not made in two weeks, but once in two weeks as well.
The larger frequency value is selected for such cases. The most frequent frequency
for the period of "weekdays" thus results in a value of one. Similarly, a value of
five is determined for a period of "weekdays".
[0091] The rule extracting unit 114 generates a rule about trip frequency, on the basis
of the maximum frequency calculated by the maximum-frequency determining unit 131,
for each destination and each period set by the period setting unit 113a.
[0092] For example, rules generated using the method (i) for the "Shop A" are "up to twice
a day", "up to once for weekdays", "up to once for days-off", and "up to five times
a week".
[0093] The unit-period frequency accumulating unit 115 accumulates frequency at which a
trip has been made for each destination and each predetermined period as in the first
embodiment.
[0094] Fig. 25 is a flowchart that shows process of the rule setting unit 130a in the navigation
apparatus 101a according to the second embodiment.
[0095] The period setting unit 113a selects one of the four periods "a day", "weekdays",
"days-off", and "a week" as a predetermined period (S120).
[0096] The maximum-frequency determining unit 131 subsequently analyzes the trip history
data 109 in order to determine a maximum frequency at which the moving object has
moved to a candidate destination during the unit period selected by the period setting
unit 113a (S121).
[0097] The rule extracting unit 114 then sets the period selected by the period setting
unit 113a and the maximum frequency determined by the maximum-frequency determining
unit 131 as a rule (S122).
[0098] Finally, the rule setting unit 130a judges whether or not rules have been extracted
for all the predetermined periods ("a day", "weekdays", "days-off", and "a week")
(S123). In the case where such rules have been extracted for all the periods (Yes
in S123), the rule setting unit 130a ends the process. When this is not the case (No
in S123), the process is repeated to extract a rule for a remaining period (the period
setting unit 113a selects remaining one from the four periods, then the process above
proceeds).
[0099] This is how rules about trip frequency, such as "up to twice a day", "up to five
time for weekdays", "up to once for days-off", and "up to five times a week" about
the "Shop A", are extracted for each destination and each predetermined period. The
extracted rules are used in a similar manner as in the first embodiment.
[0100] The rules extracted in the first embodiment and the rules extracted in the second
embodiment are not necessarily exclusive mutually and both rules may be adopted together.
For example, the rules "four times a week" and "up to once a day" may be applied to
a rule for a destination together.
[0101] Further, in the examples of the first and second embodiments, the user is provided
with information relevant to predicted destinations visually shown on the display.
Such information may be also provided audibly, for example, as voice navigation through
a speaker not shown in FIG. 8 or FIG. 28.
[0102] Further, the configurations described in the first and second embodiments have the
storage unit 104 in the destination-prediction apparatus (or the navigation apparatus).
The configurations may have storage unit 104 installed in a server on a network, and
the data handling unit 110 that obtains data stored in the storage unit 104 through
the network. Such a configuration will reduce the size of a main body of a destination-prediction
apparatus and keep the landmark data 107 or the map data 108 up to date. This will
reduce the total cost of the destination-prediction apparatus as well when a module
necessary for communication costs less than the storage unit 104. However, this will
necessitate an additional configuration to upload the data obtained by the day-and-time
detecting unit 102 or the position detecting unit 103 of the destination-prediction
apparatus to the server.
(Third Embodiment)
[0103] The third embodiment of the present invention is described below.
[0104] The first and second embodiments describe methods in which a rule for each user about
trip frequency in a predetermined period is derived on the basis of a trip history
of each user (or each moving object in which such apparatus is installed) and the
rule is used for prediction of a destination. The third embodiment describes a method
in which a destination is predicted not using a history that is based on an individual
history but a general rule about trip frequency for each destination. FIGS. 8, 23,
and 26 show configurations of a navigation apparatus 101b according to the third embodiment.
As in the one in the first embodiment, the navigation apparatus 101b is a car navigation
apparatus capable of predicting a destination with a high accuracy using regularity
of a user (or a moving object) that moves to a destination at a constant trip frequency
during a certain unit period. This navigation apparatus includes a day-and-time detecting
unit 102, a position detecting unit 103, a storage unit 104, a control unit 105, and
a display unit 106.
[0105] Also the third embodiment describes an example of a destination-prediction apparatus
according to the present invention where the destination-prediction apparatus is applied
to the navigation apparatus 101b. Components that function similarly in the navigation
apparatus 101 in the first embodiment are numbered the same and given no description
in detail.
[0106] The third embodiment differs from the first embodiment in that the storage unit 104
is provided with general-frequency-rule data 1801 and a rule setting unit 130b.
[0107] The storage unit 104 contains the general-frequency-rule data 1801 that indicates
a general rule for a predetermined category of destinations, the rule that a moving
object moves to the destinations at a trip frequency during a unit period.
[0108] The rule setting unit 130b refers to the general-frequency-rule data 1801 in order
to judge whether or not there is a rule about a candidate destination selected by
the candidate-destination selecting unit 112. When there is such a rule, the rule
setting unit 130b specifies a unit period and trip frequency of the rule to extract
it.
[0109] FIG. 27 shows an example of the general-frequency-rule data 1801 stored in the storage
unit 104. The general-frequency-rule data indicates a trip frequency rule for each
destination during a predetermined period. The trip frequency rule data is prepared
manually in view of common assumption or calculated statistically on the basis of
people's actual movement. FIG. 27 indicates that a trip to a destination categorized
as "Gas stations" was made only once for a period of "a day" and for a period of "a
week" as well. A field for "a month" has "-" that indicates that there is no applicable
rule. That there is no applicable rule means that frequency of a trip to "Gas stations"
in "a month" generally fluctuates, such as once, twice, or more, depending on frequency
at which users use their vehicles.
[0110] FIG. 27 also shows that the same rule holds true for a category of "Fast food places".
The "> 3" in a field of "Learning centers" for "a month" means that many users are
found to move to there in a period of "a month". The category mentioned here is equivalent
to the categories stored in the facility data in FIG. 10 (A).
[0111] A method to generate such a general frequency rule not manually in view of common
assumption but statistically is described below. For example, a history of information
about positions to which a trip has been actually made as stored in the trip history
data 109 according to the present invention may be used as a reference for people's
movement used in the method. Process flow, for example, a method in which a computer
automatically generates the general-frequency-rule data 1801 is specifically described
with reference to a flow chart in FIG. 28. FIG. 28 illustrates that a rule about trip
frequency is extracted for a unit period of "a day" and each category in FIG. 28.
Such a rule may be extracted for a unit period such as of "a week" or "a month" as
well.
[0112] First, a daily-trip-frequency table of four weeks, as shown in FIG. 29, is generated
for each of the users and each of the categories in a scope of statistics. A rule
about trip frequency is then extracted for each of the users and each of the categories
in the scope (S401). FIG. 29 can be read in the same manner as FIGS. 14 and 24. The
method for extracting the rule can be conceived as the one used in the maximum-frequency
determining unit 131 or the one in the rule extracting unit 114 in the second embodiment.
Here described is the case where the method "(i) Calculating the maximum frequency
during a period" is applied.
[0113] Through this process, a group of rules are extracted such as that User 1 makes a
trip once a day and that User 2 also makes a trip once a day with respect to the category
"gas stations", or that the User 1 makes a trip once a day and that the User 2 makes
a trip twice a day with respect to the category "fast food places". One of the categories
in these extracted rules (for example, gas stations in the example in FIG. 29) is
selected (S402). Then, numbers of the users who make their trips once a day, the users
who make their trips twice, and the users who make their trip three times or more
with respect to the extracted category are counted respectively (S403). FIG. 30 shows
a result of the counting. According to FIG. 30, there are 29 users to make their trips
to gas stations once a day, but no users to make their trips twice or three times
or more. The values in FIG. 30 are based on actual movements of 29 people sampled
for an experiment for collecting trip histories conducted by the inventors.
[0114] When the numbers of users have been counted, it is judged whether or not the number
of users with any of the frequencies, one, two, or three or more, is large enough
compared to a predetermined number (for example, 80% of or more than a total number
of the users) (S404). For gas stations, the total number of the users with frequencies
one, two, and three or more is 29. All of them fall under the frequency of "one";
thus, the condition is judged to be satisfied. In the case where the condition is
satisfied (Yes in S404), the frequency is extracted as a general frequency rule for
the corresponding category and period (S405). Rules extracted in such a process are
shown in FIG. 27. After the rule for gas stations is extracted, it is judged whether
or not there remains a category for which the judgment has not been made. In the case
where there does, the process returns to a step S402 to follow the steps ahead.
[0115] In the case where it is judged no in the step S404, for example, in the case in FIG.
30 where 11 people made their trips once and 11 people made their trips twice to fast
food places in "a month", the number of the users with neither of these trip frequencies
is more than 80% of the total number of the users, 22, the process advances to S406.
The rule-extracting process is completed when such judgments have been made for all
the categories.
[0116] Such process can be executed for periods of "a week" and "a month" as well though
the steps above. FIG. 30 is an excerption from statistically processed data of the
29 subjects. FIG. 27 shows excepted rules that have been extracted from this data.
[0117] The general-frequency-rule data 1801 preliminarily stores trip frequency rules extracted
manually or statistically as described above for respective destinations and predetermined
periods. The general-frequency-rule data 1801 may be newly stored or updated via a
network when the navigation apparatus 101b has a communication unit not shown in FIG.
26.
[0118] FIG. 31 is a flowchart that shows a process flow by the navigation apparatus 101b
according to the third embodiment from a start of a trip to its end. The process flow
is almost the same as shown in the flowchart in FIG. 21 for the first embodiment but
is different only in that this process flow lacks the rule extracting process in the
step S311. This is because, as mentioned above, that the process in the step 311 is
for extracting a rule of trip frequency for each user, and the third embodiment does
not have this process. In other words, not a rule for each user (or each moving object)
but only a prepared, general rule independent from a specific user is adopted (referred
to by the rule setting unit 130b) in the third embodiment.
[0119] Because a trip history of a specific user is not necessarily needed for adopting
a rule, using the general-frequency-rule data 1801 as in the third embodiment advantageously
achieves prediction with a high accuracy even soon after the user has started using
the destination-prediction apparatus according to the present invention, in other
words, when the trip history of the user has not been accumulated. Further, a characteristic
of a trip of the user may fluctuate dependent on days, weeks, or months. This may
result in that a rule extracted only using trip frequency of each user follows the
fluctuation and may become ineffective. In such a case, using a statistically reliable
rule on the basis of actual trip histories of a plurality of users will contribute
to more accurate prediction.
[0120] Although the third embodiment describes an example where a general frequency rule
is for a category as a destination, such process may use a landmark or an intersection
as a destination in order to have a similar effect.
[0121] The rules on the basis of a history of each user shown in the first and second embodiments
and the rule extracted in the third embodiment are not exclusive mutually and may
be used together. Specifically, predictions are made using general rules soon after
the user has started using the destination-prediction apparatus, and the rules are
replaced with rule of characteristics of user-specific trip frequencies as the user's
history accumulates and rules, such as that the user moves to a gas station to which
the user commutes by car six times a week, become obvious whereas the statistics show
that general users move to gas stations only once a week. This will maintain high-accuracy
of predictions from the beginning of use of the destination-prediction apparatus.
[0122] As shown in the description above, in order to achieve high-accuracy prediction,
the destination-prediction apparatus according to the present invention predicts a
destination on the basis of trip history data, taking two trip probabilities into
consideration. One trip probability is based on past trip frequency under a condition
similar to that of movement for which a destination is being predicted, and the other
is based on a rule about frequency of a trip to a destination (such as a landmark,
an intersection, or a road) in a predetermined period. This will make use of a finite
display area efficiently and prevent the user from browsing or listening to unnecessary
information by avoiding presenting such unnecessary information to the user visually
or audibly, resulting in being very effective.
[0123] A destination-prediction apparatus according to the present invention is thus described
on the basis of the first to third embodiments, but the present invention is not limited
to these embodiments. The present invention also includes variations of the embodiments
above and a different embodiment where the respective components in the first to third
embodiments above are used in any combination unless they depart from the spirit and
scope of the present invention.
[0124] For example, the actual-frequency judging unit 116, which changes ordering in a prediction
depending whether or not a rule is satisfied for a candidate destination in the first
embodiment, may change the ordering depending a remainder after subtracting an actual
frequency from a trip frequency indicated by a rule even when the rule is not satisfied
for the candidate destination. In other words, the ordering may be changed so that
prediction probability of movement of the moving object to the candidate destination
decreases more as the remainder after subtracting the actual frequency from the trip
frequency indicated by the rule is smaller. Specifically, the actual-frequency judging
unit 116 calculates a remaining period of a unit period after a present day and time
detected by the day-and-time detecting unit 102, and then divides the remainder above
by the calculated remaining period to have the remaining normalized using the remaining
period. Following this, the actual-frequency judging unit 116 may change the ordering
by the candidate-destination selecting unit 112 so that prediction probability of
movement of the moving object to the candidate destination decreases more as the calculated,
normalized remainder is smaller.
[0125] One specific example is as follows: FIG. 32 shows that there is a rule that a trip
is made "three times in a week" for the landmark A, a candidate destination, and the
trip has been made twice by the fifth day of a week. It is thus predicted that the
trip to the landmark A will be made one more time in the remaining two days. Meanwhile,
there is a rule that a trip to the landmark B, another candidate destination, is made
"eight times in a month" and the trip has been made four times by the twelfth day
of this month. It is thus predicted that the trip to the landmark B will be made four
more times in the remaining sixteen days.
[0126] In this case, the actual-frequency judging unit 116 calculates a value by dividing
the remaining trip frequency by the remaining days for each candidate destination
for which there is a rule.
[0127]
The actual-frequency judging unit 116 judges that the trip to the landmark with the
larger quotient above has larger probability to be made, and predicts destinations
with ordering according to the quotient values.
[0128] Landmarks can be determined dependently only on the remaining trip frequency when
compared in view of the same unit period. When the quotients above are equal, the
trip with less remaining days is preferably given a higher priority. For example,
it is preferable that when a remaining frequency (eight times) / a remaining period
(sixteen days) = 0.5 for the landmark C, the landmark A with less remaining days is
judged to have a higher prediction probability even though the quotient above is equal
to that of the landmark A. This is because of the same reason as the reason for the
"priority" in FIG. 12.
[0129] Here is another specific example for such remaining frequency and remaining periods.
There are a rule that a trip is made "six times in a month" for a candidate destination,
the "Shop C", and a rule that a trip is made "three times in a week" for another candidate
destination, the "Shop D". A trip history for weekday mornings shows that there were
a trip to the "Shop A" 30 times, a trip to the "Shop B" 25 times, a trip to the "Shop
C" 20 times, and a trip to the "Shop E" 13 times.
[0130] An actual frequency for the "Shop C" shows that the trip to there has been made three
times this month. There are 16 remaining days for the month. On the other hand, an
actual frequency for the "Shop D" shows that the trip to there has been made twice
in this week. There are two remaining days for the week.
[0131] With the remaining frequency of three and the remaining days of 16 for the "Shop
C", the actual-frequency judging unit 116 calculates 3 / 16 = 0.1875. With the remaining
frequency of one and the remaining days of two for the "Shop D", the actual-frequency
judging unit 116 calculates 1 / 2 = 0.5. The actual-frequency judging unit 116 thus
judges that trip to the "Shop D" has a higher prediction probability than the "Shop
C" and the drawing processing unit 117 displays the "Shop D" higher than the "Shop
C" on the display unit 106 according to the judgment.
[0132] The destinations will be displayed in the descending order of trip frequency as illustrated
in FIG. 33 (in the order of "Shop A", "Shop B", and "Shop C") when on the basis of
only frequency in a past trip history or judgment of whether or not a rule is satisfied
as shown in the first embodiment. The destinations will be displayed, as shown in
FIG. 34, in the order of the "Shop A", "Shop B", and "Shop D" in this example where
the remaining frequencies are normalized using the remaining periods and prediction
probabilities are considered higher as the resulting normalized remaining frequencies
are larger. The remaining frequencies after subtracting actual frequencies from the
trip frequencies of the rules and the remaining periods of the unit periods are taken
into consideration as well as whether or not the rules are satisfied. This will lead
to finer-tuned prediction of destinations with increased accuracy.
[0133] The actual-frequency judging unit 116 may instruct the drawing processing unit 117
to cause the display unit 106 to display a message as shown in FIG. 35 that there
remains few days when the remaining days and remaining frequencies of the rules are
taken into consideration and the quotients above are higher than a predetermined threshold.
Similarly, landmarks with quotients above equal to or larger than a predetermined
threshold (for example, 0.5) may be provided with such a message.
[0134] In the case where the actual-frequency judging unit 116 judges that a rule for a
candidate destination was not satisfied in a first period unit, the message on the
display unit 106 in a second period unit to follow the first period unit may tell
that the rule for the candidate destination was not satisfied during the first unit
period. For example, in the case where rules "twice in a week to the Shop X", "three
times in a week to the Shop Y", and "once in a week to the Shop Z" are extracted,
it is assumed that there has occurred a trip to the Shop X once, a trip to the Shop
Y twice, and a trip to the Shop Z once during a unit period. In this case, the actual-frequency
judging unit 116 may instruct the drawing processing unit 117 to cause the display
unit 106 to output a message shown in FIG. 36 that tells that the rule extracted by
the rule extracting unit 114 was not actually satisfied during the previous unit period
or a relevant message (that asks whether or not the user wants to search for a route).
The navigation apparatus may execute routing (route searching) for an efficient trip
to a landmark to which a trip was not made enough times when the user instructs the
navigation apparatus to search for a route upon the message. In the case where the
user failed a planned visit to a place during a previous unit period, this will remind
the user of it at the beginning of a subsequent unit period and allow the user to
visit the place preferentially.
[0135] Further, various methods may be applied as to starting days of a unit period set
by the period setting unit 113 and of a unit period used by the unit-period frequency
accumulating unit 115 for specifying an actual frequency. For example, a unit period
of a month may be defined as a four-week period from a day when the user starts using
the navigation apparatus, as a period from a day when the user starts using the navigation
apparatus until the day next month, or as a calendar month (or four weeks) starting
from the beginning of a subsequent month, as shown in FIG. 37 that illustrates a definition
of a unit period.
[0136] Depending on such definitions of unit periods, the rule setting unit 130 and the
unit-period frequency accumulating unit 115 operate differently. A rule may be extracted,
for example, that a trip to the landmark A is made twice a week, and the user has
actually moved to the landmark A as shown in FIGS. 38 (a) and (b) (where solid triangles
indicates occurrences). A period A indicates a period during which the landmark A
is not displayed as a prediction result or a message as shown in FIG. 22 is displayed.
[0137] Under such conditions, the unit-period frequency accumulating unit 115 starts counting
actual frequency on Mondays and ends the counting on Sundays in the case where a unit
period of "a week" is set according to the calendar (FIG. 38 (a)). In other words,
the value counted by the unit-period frequency accumulating unit 115 is reset whenever
a Monday begins. In this case, the landmark A is not excluded from prediction candidates
because of the last resetting or the message as shown in FIG. 22 is not displayed
during the period B from the starting day of the second week to the second occurrence
as shown in the second week in FIG. 38 (a).
[0138] On the other hand, the unit-period frequency accumulating unit 115 starts counting
actual frequency upon an occurrence of a movement to the landmark A and ends the counting
one week later in the case where a unit period of "a week" starts upon the occurrence
(FIG. 38 (b)). In other words, the value counted by the unit-period frequency accumulating
unit 115 is reset one week later than the first detection of the occurrence. In this
case, actual frequency is prior to a calendar period in predicting a destination and
displaying the message even when a trip occurs on different days between weeks. This
is because the length of the aforementioned period A is made constant by starting
the period unit upon the occurrences as shown in FIG. 38 (b)x.
[0139] The unit period may be therefore started according to the calendar or upon the movement
of a moving object to a candidate destination in order to take advantage of each method.
These methods may be implemented in the navigation apparatus alone or together in
a selectable manner.
Industrial Applicability
[0140] The present invention is applicable as a destination-prediction apparatus that predicts
a destination of a moving object, especially as an information terminal such as a
car navigation system installed in a vehicle or a mobile phone carried by a user.
A destination predicting method according to the present invention is also applicable
as a program on a server system that communicates with a car navigation system or
a mobile phone via a network.
1. A destination-prediction apparatus that predicts a destination of a moving object,
said destination-prediction apparatus comprising:
an obtaining unit configured to obtain trip history data indicating a trip history
of the moving object;
a position detecting unit configured to detect a present position of the moving object;
a candidate-destination selecting unit configured to search the trip history data
for trip history records containing the present position of the moving object and
select a plurality of candidate destinations and set an order thereof on a basis of
frequencies of the trip history records retrieved in the search, the trip history
data being obtained by said obtaining unit, and the present position being detected
by said position detecting unit;
a display unit configured to display, according to the order, the plurality of candidate
destinations selected by said candidate-destination selecting unit;
a rule setting unit configured to judge, with regard to at least each of the plurality
of candidate destinations, whether or not there is a rule that the moving object moves
to the candidate destination at a constant trip frequency during a certain unit period,
and set a rule that includes the unit period and the trip frequency when there is
the rule;
a unit-period frequency accumulating unit configured to specify, with reference to
the trip history data, an actual frequency with regard to one of the plurality of
the candidate destinations with regard to which said rule setting unit judges that
there is the rule, the actual frequency being a frequency at which the moving object
has moved to the candidate destination during the unit period and
an actual-frequency judging unit configured to make a judgment, with regard to each
of the plurality of candidate destinations, on whether or not the actual frequency
specified by said unit-period frequency accumulating unit satisfies the rule by judging
whether or not the actual frequency has reached the trip frequency included in the
rule,
said display unit being configured to display, when said actual-frequency judging
unit judges that the actual frequency satisfies the rule, the plurality of candidate
destinations so that a result of the judgment is reflected thereon.
2. The destination-prediction apparatus according to Claim 1,
wherein said actual-frequency judging unit is configured to change the order, according
to a result of the judgment, by placing a lower priority on the candidate destination
for which the rule is satisfied than when the rule is not satisfied, or a high priority
on the candidate destination for which the rule is not satisfied than when the rule
is satisfied, and
said display unit is configured to display the plurality of candidate destinations
according to the order changed by said actual-frequency judging unit.
3. The destination-prediction apparatus according to Claim 1,
wherein said actual-frequency judging unit is configured to change the order set by
said candidate-destination selecting unit by excluding, from the plurality of candidate
destinations, the candidate destination for which the rule is satisfied, and
said display unit is configured to display the plurality of candidate destinations
according to the order changed by said actual-frequency judging unit.
4. The destination-prediction apparatus according to Claim 1,
wherein said display unit is configured to display an indication about the candidate
destination with regard to which said actual-frequency judging unit judges that the
rule is satisfied, the indication indicating that the rule is satisfied for the candidate
destination.
5. The destination-prediction apparatus according to Claim 1,
wherein said display unit is configured to display an indication about the candidate
destination with regard to which said actual-frequency judging unit judges that the
rule was not satisfied during a first unit period, in a second unit period that follows
the first period, the indication indicating that the rule was not satisfied for the
candidate destination in the first unit period.
6. The destination-prediction apparatus according to Claim 1,
wherein said actual-frequency judging unit is configured to reduce a prediction probability
that the moving object moves to the candidate destination to lower when, according
to an result of the judgment, the rule is satisfied for the candidate destination
than when a the rule is not satisfied, and change the order set by said candidate-destination
selecting unit, and
said display unit is configured to display the plurality of candidate destinations
according to the order changed by said actual-frequency judging unit.
7. The destination-prediction apparatus according to Claim 1,
wherein said rule setting unit includes:
a unit-period setting unit configured to set the unit period; and
a rule extracting unit configured to specify, by analyzing the trip history data,
a frequency at which the moving object moves to the candidate destination during the
unit period set by said unit-period setting unit, and set the specified frequency
during the unit period as the rule.
8. The destination-prediction apparatus according to Claim 7,
wherein said period setting unit is configured to specify, with reference to the trip
history data, a period during which the moving object moves to the candidate destination
at a constant frequency, and set the specified period as the unit period.
9. The destination-prediction apparatus according to Claim 7,
wherein said rule setting unit further includes a maximum-frequency determining unit
configured to determine, by analyzing the trip history data, a maximum of the frequency
at which the moving object moves to the candidate destination during the unit period
set by said period setting unit, and said rule extracting unit is configured to set
the maximum of the frequency during the unit period as the rule, the maximum being
determined by said maximum-frequency determining unit.
10. The destination-prediction apparatus according to Claim 9,
wherein said maximum-frequency determining unit is configured to calculate a largest
value of frequencies which are in the trip history data and at which the moving object
has moved to the candidate destination during the unit period, and determine the calculated
largest value as the maximum.
11. The destination-prediction apparatus according to Claim 9,
wherein said maximum-frequency determining unit is configured to specify numbers of
occurrences of frequencies which are in the trip history data and at which the moving
object has moved to the candidate destination during the unit period, and determine
a frequency having a largest number of occurrences as the maximum.
12. The destination-prediction apparatus according to Claim 1,
wherein the obtaining unit is further configured to obtain general-frequency-rule
data that indicates a general rule for a predetermined category of destinations, the
general rule being that a moving object moves to the destinations at a trip frequency
during a unit period, and
said rule setting unit is configured to judge whether or not there is the rule and
determine the unit period and the trip frequency with reference to the general-frequency-rule
data.
13. The destination-prediction apparatus according to Claim 1,
wherein said actual-frequency judging unit is further configured to change the order
set by said candidate-destination selecting unit so that a prediction probability
of movement of the moving object to a candidate destination for which the rule is
not satisfied decreases more as a remainder after subtracting the actual frequency
from the trip frequency is smaller, the candidate destination being among the candidate
destinations with regard to which said rule setting unit judges that there are rules.
14. The destination-prediction apparatus according to Claim 13,
further comprising a day-and-time detecting unit configured to detect a present day
and time,
wherein said actual-frequency judging unit is configured to calculate a remaining
period of the unit period after the present day and time detected by the day-and-time
detecting unit, and divide the remainder by the calculated remaining period to have
the remainder normalized using the remaining period, and change the order set by the
candidate-destination selecting unit so that a prediction probability of movement
of the moving object to the candidate destination decreases more as the calculated,
normalized remainder is smaller.
15. The destination-prediction apparatus according to Claim 1, further comprising:
a day-and-time detecting unit configured to detect a present day and time; and
a storage unit configured to store, as trip history data, a present position detected
by said position detecting unit while associating the present position with the present
day and time detected by said day-and-time detecting unit,
wherein said obtaining unit is configured to obtain the trip history data from said
storage unit.
16. The destination-prediction apparatus according to Claim 1,
wherein the unit period is a period according to a calendar or a period starting when
the moving object has moved to a candidate destination.
17. A navigation apparatus that supports movement of a moving object,
said navigation apparatus comprising said destination-prediction apparatus according
to Claim 1.
18. A destination-prediction method for predicting a destination of a moving object, the
destination-prediction method comprising:
obtaining trip history data indicating a trip history of the moving object;
detecting a present position of the moving object;
searching the trip history data for trip history records that contains the present
position of the moving object, and then selecting a plurality of candidate destinations
and setting an order thereof on a basis of frequencies of the trip history records
retrieved in the searching, the trip history data obtained in said obtaining, and
the present position detected in said detecting;
displaying, according to the order, the plurality of candidate destinations selected
in said selecting;
judging, with regard to at least each of the plurality of candidate destinations,
whether or not there is a rule that the moving object moves to the candidate destination
at a constant trip frequency during a certain unit period, and then setting a rule
that includes the unit period and the trip frequency when there is the rule;
specifying, with reference to the trip history data, an actual frequency with regard
to one of the plurality of the candidate destinations with regard to which said judging
judges that there is the rule, the actual frequency being a frequency at which the
moving object has moved to the candidate destination during the unit period; and
making a judgment, with regard to each of the plurality of candidate destinations,
on whether or not the actual frequency specified in said specifying satisfies the
rule by judging whether or not the actual frequency has reached the trip frequency
included in the rule,
said displaying displays, when said judging judges that the actual frequency satisfies
the rule, the plurality of candidate destinations so that a result of the judgment
is reflected thereon.
19. A program for a destination prediction apparatus that predicts a destination of a
moving object,
said program causing a computer to execute the destination-prediction method according
to Claim 18.